Abstract (inglese)

In order to study the neural behavior, scientists employ multichannel probes where each electrode records a mixture of spike trains from surrounding neurons. A first necessary step is the individuation and the separation of signal from different sources, associating each detected spike to the neuron of origin. Many spike sorting algorithms based on different principles have been developed for this purpose, but there is still no consensus on which is the best method.

This thesis addresses the issue of impulsive signal classification in the Neurophysiological framework presenting a novel spike sorting algorithm named Multi-Channel Inversion for Spike Classification (MCI4SC). The new method exploits multichannel information related to neuron positions, and makes a distinctive use of the mixing matrix associated to the measurement channel.
In particular, inverting many matrices derived by the mixing one, the method is able to handle the disadvantageous, but typical, situation where there are more recorded neurons than recording sensors, under the reasonable hypothesis that the number of simultaneously firing neurons is lower than or equal to the number of sensors.
Another distinguishing feature of MCI4SC algorithm and its implementation is the use of the Wavelet Packet Transform. This tool has been used to estimate the ratio between spike amplitudes in different channels, thus leading to a consistent estimation of the mixing matrix components even in case of low signal to noise ratio.

The MCI4SC algorithm has been applied on experimental data with human supervision on the threshold setting. Good spike sorting results have been obtained for bursting Purkinje Cells with varying waveform and amplitude spikes, as well as for noisy neurons in Locust antennal lobe.
Compared with an algorithm based on the Markov Chain Monte Carlo, the MCI4SC algorithm has at least comparable efficiency with a much lower computational time, in addition to the important capability of overlapping spike resolution.
This makes the new algorithm presented in this thesis a reliable and competitive tool in the spike sorting context.